Sample of Elegant Predictive Analytic Data Visualizations

  1. Healthcare .  In this visualization, GE took 500,000 records from the millions in its electronic medical record database, and calculated the out of pocket and insurer cost of a handful of chronic conditions by age.  One of the few both uses of and displays/designs of a radar graph, the visual changes as one increases and decreases age.  This also has a predictive analytics component in that it answers a user’s question “What if I develop a case of hypertension, what will that cost me when I’m 65?”  Like any good analytic, being able to see the data brings up more actionable and specific questions that this analytics doesn’t answer but the data set could.
  2. Digital Media.  This visualization is part of The New York Talk Exchange, a visualization project developed by the Senseable City Lab at MIT.  Perhaps the potential applications of the analytics are as explosive or more than the specific data they used in this case.  The analytic shows starting or sourcing neighborhood within NYC and to where their communications via the AT&T network were destined.  Users can see the frequency distribution of endpoints, and by comparison who was talking to whom geographically across sister boroughs.  Imagine creating a site map of your web site, application, team or workflow, and seeing the frequency of where the user, function, business process or capital goes next.  Predictive analytics can say: “If we invest in area x where is that capital, profit opportunity, or waste most likely to go next, and how does that change if I make another investment?”  Perhaps only this specific visualization is best for that type of comparative predictive analytic.
  3. Retail.  According to Well Formed Data, Sankey Diagrams and stacked bar charts informed this (4MB .pdf download) time series visualization of how medical journals in related fields merged into a cohesive ‘basket’ of journals in the emergent field of neuroscience.  While on the surface not retail related, it points to a very compelling—and as yet a visualization I’ve never seen produced— which would explain how specific products that drive volume and profit affinitize into specific types of market baskets.  Replace a) each journal with a specific retail product, which the user can color code at run-time for visualization, perhaps color-coded for on/off promotion, b) the eigenfactor of each journal which is represented by the weight/width of the specific line with the amount of profit or volume the item produces—again the user can choose profit or volume or some other measure at run time, c) the ten or so portfolio of ending blocks of journals as specific types of market baskets, and d) the breadth of starting lines moves from medical disciplines to aisles or departments in a store.  In effect what you have here as a decade long time series one has compressed into a single, specific shopping trip.  Data can include many trips, a single store, one day or years of data.  The predictive angle is to be able to answer questions like “If I promote this item, does it move away from its core ‘7 items per basket, quick trip’ basket into a ‘destination item weekly stock-up’ basket?”  One can also look historically how different store consumers shop categories, volume vs. profit items, and more.

These predictive analytic visualizations start with healthcare as the least complex and become more complex.  Depending on how frequently the user needs the data updated, the amount of data (in the digital media/telco example clearly tens or hundreds of gigabytes), the processing (in the retail example, calculating eigenvalues in an analytic that could be analyzed hourly, especially for promotional out-of-stocks, for example), and the speed required means a predictive analytic visualization is not something to try at home with an off-the-shelf database platform and hardware.